I think it should be straightforward to express this using structured
streaming. You could ensure that data from a given partition ID is
processed serially by performing a group by on the partition column.
spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "...")
.option("subscribe", "t0,t1")
.load()
.groupBy($"partition")
.writeStream
.foreach(... code to write to cassandra ...)
On Thu, Mar 16, 2017 at 8:10 AM, Cody Koeninger <c...@koeninger.org> wrote:
> Spark just really isn't a good fit for trying to pin particular
> computation to a particular executor, especially if you're relying on that
> for correctness.
>
> On Thu, Mar 16, 2017 at 7:16 AM, OUASSAIDI, Sami <sami.ouassa...@mind7.fr>
> wrote:
>
>>
>> Hi all,
>>
>> So I need to specify how an executor should consume data from a kafka
>> topic.
>>
>> Let's say I have 2 topics : t0 and t1 with two partitions each, and two
>> executors e0 and e1 (both can be on the same node so assign strategy does
>> not work since in the case of a multi executor node it works based on round
>> robin scheduling, whatever first available executor consumes the topic
>> partition )
>>
>> What I would like to do is make e0 consume partition 0 from both t0 and
>> t1 while e1 consumes partition 1 from the t0 and t1. Is there no way around
>> it except messing with scheduling ? If so what's the best approach.
>>
>> The reason for doing so is that executors will write to a cassandra
>> database and since we will be in a parallelized context one executor might
>> "collide" with another and therefore data will be lost, by assigning a
>> partition I want to force the executor to process the data sequentially.
>>
>> Thanks
>> Sami
>> --
>> *Mind7 Consulting*
>>
>> Sami Ouassaid | Consultant Big Data | sami.ouassa...@mind7.com
>> __
>>
>> 64 Rue Taitbout, 75009 Paris
>> ᐧ
>>
>
>
